The present disclosure relates generally to signal processing of media data, such as signal processing of audio data for quality enhancement.
Equalization processing of an audio signal is commonly used to alter the frequency response of an audio signal to be within a user specified range and is typically achieved by using an equalization filter whose frequency response can be adjusted by a user for one or more reasons, such as the resulting audio signal having improved fidelity, emphasizes certain frequencies or ranges of frequencies, has undesired frequency components such as noise removed, and/or matches perceived timbre of multiple audio signal pieces, such as songs on a CD or multiple compressed MP3 audio signal files. Audio signal equalization is also commonly used in film and television production to improve the quality of the sound, modify and/or match the timbre of audio signal in different scenes or to match individual audio signal streams which comprise a film or television soundtrack. Audio signal equalization can also be used to modify specific frequencies and to make audio signal perceptually louder, as well as to compensate for frequency dependent deficiencies in an audio signal reproduction system.
Background Art: Traditional Equalization:
Many kinds of traditional equalization filters are known, and each has a different behavior with regard to the frequencies that they attenuate or boost (add gain). With traditional equalization, filtering is applied to an audio signal in order to change its frequency spectrum. A peak equalizer raises or lowers a range of frequencies around a central point in a bell shape. A peaking equalizer, with controls to adjust the level (attenuation or gain), filter bandwidth (denoted by Q) and center frequency is called a Parametric Equalizer, with the parameters: gain, bandwidth, and center frequency. A similar peaking equalizer but with no control of the bandwidth, e.g., a bandwidth fixed by the filter designer, is sometimes called a Quasi-Parametric Equalizer or a Semi-Parametric Equalizer.
A Pass Filter Equalizer attenuates either high or low frequencies while allowing other frequencies to pass unfiltered. Such filters include a Low-Pass filter, a High-Pass filter, and a Band-Pass filter that combines the properties of a High-Pass and Low-Pass filter.
Shelving type equalizers increase or attenuate the level of a wide range of frequencies by a fixed amount. A Low-Shelf will affect low frequencies up to a certain point and then above that point will have little effect. A High-Shelf affects the level of high frequencies; while below a certain point, the low frequencies are unaffected.
In many equalization hardware and software implementations, it is not uncommon for all three types of equalizer filters, Peak, Pass and Shelving, to be part of the signal processing path to modify an audio signal.
Another common type of equalizer is a Graphic Equalizer, which includes controllers such as a bank of sliders or other controllers for boosting and cutting different bands (frequency ranges) of an audio signal. Normally, these bands are tight enough to give at least 3 dB or 6 dB maximum effects for neighboring bands, and cover the range from around 20 Hz to 20 kHz. An example of a simple Graphic Equalizer is a 4-band equalizer that might have bands at 20 Hz, 200 Hz, 2 kHz and 20 kHz. A typical Graphic Equalizer for live sound reinforcement might have as many as 24 or 32 bands.
The choice of equalizer is chosen to meet the equalization task and the characteristics of the signal to be processed. The most common types in audio work are the Shelf and the Parametric Equalizers. Each may be implemented in the analog or digital domains. Each equalizer is capable of adding gain or attenuating a signal over a defined band(s) of spectral frequencies. Out-of-band signals are not processed.
Such equalizations, however, do not take into account the time varying nature of the input signal. And, in most cases, they accomplish their equalization tasks while adding undesirable effects (e.g. noise) to the output of the processed signal. The issues noted are particularly significant when processing audio signals due to the deleterious effects that can manifest in unintentional audible inconsistencies and noise. This invention accomplishes the equalization goals without developing these negative issues.
When considering the nature of audio signals in particular, many audio signals consist of one or more “fundamental tones” and additional tones that are related to the fundamental tones. The relationship is one of submultiples or multiples of the fundamental tone. These tones are expressed in frequency terms. For example, a musical instrument may sound an A440 note as a fundamental tone and produce sub-harmonics of A220, A110; as well as harmonics of A880, A1320, and so on. The relative strengths of the tones can vary greatly and influence the perceived musical instrument and tone being played. This nature of audio signals is generally true for voice and musical instruments of string, reed, and wind. This general understanding of audio sound does not apply to instruments like drums and percussive sounds because these instruments do not usually produce harmonically related sound. The analytic and visual display of the tonal content of these composite signals are typically expressed and explored by plotting the frequency content as frequency vs. tonal magnitude. These plots are known as “Spectral” or “Frequency Spectrum” plots.
It is important to note that time variant signals, by definition, are not spectrally stationary. That is, the spectral frequency and magnitude of the components that make up the sound are continually changing over time. Consider an audio signal that is not a singular, constant tone. That signal presents the case where its spectrum over time is not constant. Over time, the spectral fundamental tone(s) and related components are moving within the audio band as well as their magnitudes. Also, fundamentals that are constant may have harmonics (and sub-harmonics) that vary in magnitudes as the tones mature in time. This is the typical case of a person speaking, singing or playing a musical instrument, as examples.
FIG. 2 shows the measured spectrum of a 6-string acoustic guitar. Shown are spectrum of individual strings and a chord struck on the instrument. Notable are the complicated spectral plots and their varying spectral profiles, changing for different strings. In practice, as the different notes of this instrument are sounded, it is clear that a static equalization technique devised for one spectrum will not suffice for the others. Unintended equalization of spectral components or added noise content may result.
Of the known equalization processes, which is best to accomplish the equalization goals?
A Shelving Equalizer is a good choice from the point of view of processing the tone wherever it appears at any given time in the spectral band of equalization interest. The tone will be processed as desired. However, because the Shelf Equalizer processes the entire band of interest, the spectrum where there is no signal is also processed. A typical example is shown in FIG. 3. If the equalizer is adding gain, the noise will also be equalized. This will raise the noise content of the processed signal, certainly an undesirable effect and cannot be avoided. This is illustrated in FIG. 4.
A Parametric Equalizer is another equalization process implemented to achieve the desired goals. It is a spectrum bandwidth-confined equalization process applied to the input signal. As the signal spectrum moves over time and is beyond the defined band of equalizer affectivity, the Parametric Equalizer processing will not act upon the signal; or at best, provide uneven magnitude equalization. Thus, the equalization goal will not be attained, and the noise content will be increased when adding gain to the spectrum specified, as in the case of the Shelving Equalizer.
FIG. 5 illustrates a parametric equalizer with two different Q parameters; low Q and high Q. An equalizer with a high Q will produce an equalization of small bandwidth. The opposite is true of a low Q parametric equalizer. In the case of a high Q equalizer, the equalization is well accomplished for a static frequency component, but not a practical situation. A low Q encompasses a higher bandwidth. This technique may accomplish the desired equalization; however, it will do so while adding noise to the output signal in a similar fashion to the Shelving Equalization filter. It may also unintentionally equalize spectral components. FIG. 6 illustrates an example of a non-static input spectrum equalized by a static parametric equalizer.
The operator of the equalizer also plays a role in choosing correct parameters for the equalizer in order to accomplish the desired equalization task. In the case of the Shelving Equalizer, a frequency is chosen to define the processing band of interest. An operator typically will choose this parameter by identifying the audio source and relying upon an experienced determination. When using a Parametric Equalizer, it becomes much more difficult. The center frequency and width of the filter(s), Q, must be determined. Typically, this can only be accurately accomplished with the aid of instrumentation to define the nature of the signal to be equalized. And, it will only be accurate for a given time. At other times, when the signal's frequency content is out of the processing band, the noise level of the processed signal will be greater, as noted earlier.